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Related Questions
- What are some common biases present in language models and how can data augmentation help mitigate them?
- Can you provide examples of how data augmentation can be used to improve the fairness of language models in healthcare, such as in medical diagnosis or patient outcomes?
- How can data augmentation be used to address biases in language models related to demographic or socioeconomic factors in the finance domain?
- What are some strategies for creating and using diverse and representative datasets through data augmentation to improve fairness in language models?
- Can you explain how data augmentation can help reduce the impact of confounding variables on language model performance and fairness?
- How can data augmentation be used to improve the fairness of language models in specific domains by increasing the diversity of training data?
- What are some potential challenges or limitations of using data augmentation to improve the fairness of language models, and how can they be addressed?
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